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    <div align="right">Last update : July 2000</div>
    <p>
      <b>ftuneq</b> -  Fischer ratio for samples of unequal size.  </p>
    <h3>
      <font color="blue">Calling Sequence</font>
    </h3>
    <dl>
      <dd>
        <tt>f=ftuneq(sample1[,sample2[,sample3]...]])  </tt>
      </dd>
      <dd>
        <tt>[f,p]=ftuneq(sample1[,sample2[,sample3]...]])  </tt>
      </dd>
    </dl>
    <h3>
      <font color="blue">Parameters</font>
    </h3>
    <ul>
      <li>
        <tt>
          <b>sample1, sample2, sample3,...  </b>
        </tt>: real or complex matrix of any type</li>
    </ul>
    <h3>
      <font color="blue">Description</font>
    </h3>
    <p>
    This function computes the F ratio for samples of unequal
    size.</p>
    <p>
    "The most  efficient design is  to make all  samples the
    same  size n.   However when  this is  nor  feasible, it
    still  is possible  to modify  the  ANOVA calculations."
    Note  that  the  definition  of  xbarbar  is  no  longer
    mean(xbar), but  rather a weighted  average with weights
    ni.  Additionnally  it gives (in  p) the p-value  of the
    computed Fischer ratio.</p>
    <p>
    Given a number  a of samples each of  them composed of n_i
    (i from 1  to a) observations this fonction  computes in f
    the Fischer  ratio (it is  the ratio between nr  times the
    variance  of the  means of  samples  and the  mean of  the
    variances of each sample).</p>
    <p>
      <tt>
        <b> f=ftest(samples) </b>
      </tt> computes the Fischer ratio of the
    nc samples whose  values are in the columns  of the matrix
    <tt>
        <b>samples</b>
      </tt>. Each one of these samples is composed of nr
    values. (The  Fischer ratio is the ratio  between nr times
    the  variance of  the means  of  samples and  the mean  of
    variances of each sample)</p>
    <p>
      <tt>
        <b> [f,p]=ftest(samples) </b>
      </tt> gives in p the p-value of the
    computed Fischer ratio f.</p>
    <h3>
      <font color="blue">References</font>
    </h3>
    <dl>
      <p>
    Wonacott, T.H. &amp;  Wonacott, R.J.; Introductory Statistics, J.Wiley &amp; Sons, 1990.</p>
    </dl>
    <h3>
      <font color="blue">Examples</font>
    </h3>
    <pre>

samples=[46 55 54;53 54 50; 49 58 51;50 61 51;46 52 49]
[f,p]=ftest(samples)
 
  </pre>
    <h3>
      <font color="blue">See Also</font>
    </h3>
    <p>
      <a href="ftuneq.htm">
        <tt>
          <b>ftuneq</b>
        </tt>
      </a>,&nbsp;&nbsp;</p>
    <h3>
      <font color="blue">Author</font>
    </h3>
    <p> Carlos Klimann</p>
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